Case-based reasoning
Knowledge acquisition and learning by experience—the role of case-specific knowledge
Machine learning and knowledge acquisition
Inside Case-Based Reasoning
Integrating E-Commerce and Data Mining: Architecture and Challenges
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
AST: Support for Algorithm Selection with a CBR Approach
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Fusion of Meta-knowledge and Meta-data for Case-Based Model Selection
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
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Developing and applying data mining processes are often very complex tasks to users without deep knowledge in this domain, particularly when such tasks involve clickstream data processing. One important and known challenge arises in the selection of mining methods to apply on a specific data analysis problem, trying to get better and useful results for a particular goal. Our approach to address this challenge relies on the reuse of the acquired experience from similar problems, which had provided successful mining processes in the past. In order to accomplish such goal, we implemented a prototype mining plans selection system, based on the Case-Based Reasoning paradigm. In this paper we explain how this paradigm and the implemented system may be explored to assist decisions on the data mining or Web usage mining specific scope. Additionally, we also identify the underlying issues and the approaches that were followed.